This repo contains an implementation of reMap (relabeling Metabolic pathway data with groups) a simple, and yet, generic framework, that performs relabeling examples to a different set of labels, characterized as pathway groups (or "bags"), where a group comprises with correlated pathways. To obtain groups, any models from CHAP package can be employed. After obtaining groups, reMap preforms relabeling by alternating between 1) assigning groups to each sample (or feed-forward) and 2) updating reMap's parameters (or feed-backward). reMap's effectiveness were evaluated on metabolic pathway prediction (using leADS) where resulting performance metrics equaled or exceeded other prediction methods on organismal genomes with improved pathway prediction outcomes.
See tutorials on the GitHub wiki page for more information and guidelines.
If you find reMap useful in your research, please consider citing the following paper:
- M. A. Basher, Abdur Rahman and Hallam, Steven J.. "Relabeling metabolic pathway data with groups to improve prediction outcomes", bioRxiv (2021).
For any inquiries, please contact Steven Hallam and Abdurrahman Abul-Basher at: shallam@mail.ubc.ca and arbasher@student.ubc.ca